Monthly Archives: June 2013

Well, not “everything”. But every measure on which decisions of value depend (e.g., book purchases, dating opportunities, or tenure) can and will be manipulated.
And if the measure depends on user-contributed content distributed on an open platform, the manipulation often will be easy and low cost, and thus we should expect to see it happen a lot. This is a big problem for “big data” applications.
This point has been the theme of many posts I’ve made here. Today, a new example: citations of scholarly work. One of the standard, often highly-valued (as in, makes a real difference to tenure decisions, salary increases and outside job offers) measures of the impact of a scholar’s work is how often it is cited in the published work of other scholars. ISI Thompson has been providing citations indices for many years. ISI is not so easy to manipulate because — though it depends on user-contributed content (articles by one scholar that cite the work of another) — that content is distributed on closed platforms (ISI only indexes citations from a set of published journals that have editorial boards which protect their reputation and brand by screening what they publish).
But over the past several years, scholars have increasingly relied on Google Scholar (and sometimes Microsoft Academic) to count citations. Google Scholar indexes citations from pretty much anything that appears to be a scholarly article that is reachable by the Google spiders crawling the open web. So, for example, it includes citations in self-published articles, or e-prints of articles published elsewhere. Thus, Google Scholar citation counts depends on user-contributed content distributed on an open platform (the open web).
And, lo and behold, it’s relatively easy to manipulate such citation counts, as demonstrated by a recent scholarly paper that did so: Delgado Lopez-Cozar, Emilio; Robinson-Garcia, Nicolas; Torres Salinas, Daniel (2012). Manipulating Google Scholar Citations and Google Scholar Metrics: simple, easy and tempting. EC3 Working Papers 6: 29 May, 2012, available as http://arxiv.org/abs/1212.0638v2.
Their method was simple: they created some fake papers that cited other papers, and published the fake papers on the Web. Google’s spider dutifully found them and increased the citation counts for the real papers that these fake papers “cited”.
The lesson is simple: for every measure that depends on user-contributed content on an open platform, if valuable decisions depend on it, we should assume that it is vulnerable to manipulation. This is a sad and ugly fact about a lot of new opportunities for measurement (“big data”), and one that we must start to address. The economics are unavoidable: the cost of manipulation is low, so if there is much value to doing so, it will be manipulated. We have to think about ways to increase the cost of manipulating, if we don’t want to lose the value of the data.